Approaches based on artificial intelligence and the internet of intelligent things to prevent the spread of COVID-19: Scoping review
Open accessPublished version of a preprint
Abstract
BACKGROUND: Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. OBJECTIVE: The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. METHODS: We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. RESULTS: Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. CONCLUSIONS: We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.
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These top technologies are merging today to form artificial-intelligence-of-things (AIoT) to achieve more efficient IoT operations, improve decision-making and human-machine interactions, and enhance data management and analytics.
“Unsurprisingly, these top technologies are merging today to form artificial-intelligence-of-things (AIoT) [88] to achieve more efficient IoT operations, improve decision-making and human-machine interactions, and enhance data management and analytics [89].”
From The sustainability of artificial intelligence: an urbanistic viewpoint from the lens of smart and sustainable cities (2020) · cites this paper as [88]
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- Authors
- Adly, A. S., Adly, A. S., & Adly, M. S.
- Affiliations
- Beni-Suef University (EG)
- Cairo University (EG)
- Corresponding author
- Adly, Mahmoud Sedky
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-08-10
- Volume
- 22
- Issue
- 8
- Pages
- e19104
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC7423390
- OpenAlex
- W3037105702
- Cited by
- 138
- References
- 86
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 15 · 2023: 20 · 2022: 38 · 2021: 48 · 2020: 11
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Keywords
- COVID-19 pandemic, artificial intelligence, SARS-CoV-2, pandemic prevention, case finding, large-scale screening, pneumonia screening, resource allocation, disease prediction, modeling and simulation, medical robotics, quarantine, health data integration
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Artificial Intelligence, Health Personnel, Humans, Pneumonia, Viral, Risk Factors, Coronavirus Infections, Internet, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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